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This study reunites areal interpolation with the isopleth mapping process to construct an inferred larger scale isopleth map. Intelligent areal interpolation is used to construct two types of population density surfaces that are used as inputs for pycnophylactic interpolation of an isopleth surface. One is a target zone population density surface (TZPDS) and the other is a control zone population density surface (CZPDS). Results suggest that an inferred isopleth map with remote sensing control data is a better surface depiction than an isopleth map without any control data, and the quality of such an isopleth map is further improved by enhancing the remote sensing data with residential parcel information. A CZPDS-derived intelligent isopleth map also has more peaks and variations in population distribution patterns than does a TZPDS-derived one due to the larger scale of the control data.

This paper is an analysis of the spatial distribution of housing vacancies in Detroit in four census years: 1980, 1990, 2000, and 2010. Our analysis is largely grounded in the contexts of race and class. We use both cartographic and statistical methods to illustrate the distribution of vacancies at the census tract level and to model the conditions that contribute to vacancy rates. A cartographic analysis of the spatial distribution of housing vacancies over time in Detroit indicates that a weak general pattern of outward diffusion occurred from 2000 to 2010. A regression analysis indicates there is a structural pattern of race and class characteristics at the tract level, as measured respectively by percent White—tied to the potential for White flight—and by unemployment rates—tied to financial factors of housing abandonment, that are good predictors of housing vacancy rates over fairly long time periods. Other good predictors are a tract’s percentage of rental housing and, to a lesser degree, th...

William Bunge's Fitzgerald: Portrait of a Revolution, initially published in 1971, is an enthralling verbal and visual account of the historical and geographical development of a one-square-mile neighborhood in Detroit. The original analysis of the Fitzgerald neighborhood was based on intensive field-based research conducted in a theoretical context of race and racism. The research reported here maintains that context but updates Fitzgerald's account of the neighborhood's built environment through a spatial analysis that uses parcel-by-parcel data generated in Google Earth and Google Street View instead of data collected in the field. Current spatial patterns of deterioration in the built environment are similar to those described in Fitzgerald, but positive sites are also apparent and often colocated with negative ones.

Local regression methods such as geographically weighted regression (GWR) can provide specific information about individual locations (or places) in spatial analysis that is useful for mapping nonstationary covariate relationships. However, the distance-based weighting schemes used in GWR are only adaptable for spatial objects that are point or area features. In particular, spatial object-pairs pose a challenge for local analysis because they have a linear dimensionality rather than a point dimensionality. This paper proposes using an alternative local regression model – quantile regression (QR) – for investigating the stationarity of regression parameters with respect to these linear features as well as facilitating the visualization of the results. An empirical example of a gravity model analysis of trade patterns within Europe is used to illustrate the utility of the proposed method.

Control data are critical for improving areal interpolation results. Remotely sensed imagery, road network, and parcels are the three most commonly used ancillary data for areal interpolation of population. Meanwhile, the open access geographic data generated by social networks is emerging as an alternative control data that can be related to the distribution of population. This study evaluates the effectiveness of geo-located night-time tweets data as ancillary information and its combination with the three commonly used ancillary datasets in intelligent areal interpolation. Due to the skewed Twitter user age, the other purpose of this study is to test the effect of age bias control data on estimation of different age group populations. Results suggest that geo-located tweets as single control data does not perform as well as the three other control layers for total population and all age-specific population groups. However, the noticeable enhancement effect of Twitter data on other control data, especially for age groups with a high percentage of Twitter users, suggests that it helps to better reflect population distribution by increasing variation in densities within a residential area delineated by other control data.

Proximity to wetlands and water bodies can be considered an amenity (for open space and recreation value), as well as a possible nuisance (with the potential for flooding or development restrictions), although the overall effect may be different depending on location. Studies of the impacts of wetlands and water on housing prices can also be prone to spatial autocorrelation problems arising from omitted unobservables. McMillen and Redfearn explain that locally weighted regressions (LWRs) can address spatial autocorrelation. In addition to ordinary least squares (OLS), we use LWR to control for spatial effects and analyze how proximity to water bodies and wetland areas impact real sales prices of homes in one Connecticut town in 2000–2009. With OLS regressions, proximity to wetland areas and water bodies are insignificant determinants of the real sale price of homes. When we control for spatial effects with a nonparametric (i.e., LWR) approach, the significance of the water variable is different than from OLS—while greater distance from wetlands leads to an insignificant relationship with housing price, the water distance effect becomes negative and significant. These results imply that incorporating potential spatial heterogeneity in the data is crucial for accurately estimating the direction, magnitudes, and statistical significance of the relationships between environmental variables and housing prices.

This article extends earlier work on visualizing parameter estimates for spatial analysis. Robust parameters are those that are resistant to outliers, and so are order-based rather than moment-based in their derivation. The principles of geographical weighting of descriptive statistics are reviewed and applied to the development and computation of geographically weighted quantile graphs. The principles are also applied to the development and mapping of robust geographically weighted regression models. The robust statistics and parameters developed in this article are especially useful for visual exploratory data analysis because they provide local information in two contexts: with respect to spatial proximity and with respect to proximity in the quantiles of the variable of interest. The suitability of the results for visualization in maps and quantile plots is demonstrated.

In spatial analysis, population frequently is aggregated into source units having an areal extent. When using such data in a flow model, distances are calculated as an average between each source unit and a set of destinations. In a network, this average distance might be the shortest path between a destination and the centroid of a source unit. However, population is never concentrated at centroids nor is it uniformly distributed within each spatial unit. In urban areas, it is more likely located proximate to the road system that traverses most areal units. This article presents a method for interpolating areally aggregated data to the segments of the road network bounding an areal unit using a dasymetric approach. A case study for Phoenix, Arizona, compares using a network-interpolated population distribution with the area-based approach for the problem of defining service areas for a given set of facilities. A comparison of the road network used, the total demand within each service area, and the total weighted travel distance to facilities shows that the areal-based method underestimates the portion of road network used in travel and misestimates both the expected demand of each service area and the overall travel distance to a facility. 在空间分析中，群体频繁的集聚于具有一定面积范围的源区域。当在流模型中应用这类数据时，距离计算往往采用每个源区域与目标区域之间的平均值。在一个网络中，这种平均距离可能是终点区域和源区域质心的最短路径。然而，人口并不集中于质心或均匀分布于每个空间单元中。在城市地区，人口最有可能位于横贯多数空间单元的道路系统周围。基于密度插值的方法，本文提出了将空间聚集数据插值至以道路网络为边界的区域单元。以亚利桑那州菲尼克斯进行实证分析，针对给定服务设施集合下提供服务范围的问题，比较了道路网络插值和基于区域插值方法得到的人口分布。利用道路网络的每个服务区的总需求和到服务设施的总体加权通行距离进行比较，基于区域的插值方法低估了用于通行的道路网络比例，同时也误估了每个服务区的期望需求和到一个服务设施的总体通行距离。

Areal interpolation is used to transfer attribute data between geographically incongruous zonal systems. Remotely sensed land cover data are widely used in intelligent areal interpolation methods to solve this problem. This article examines the usefulness of different publicly available remotely sensed land cover data sets as ancillary data used in conjunction with different areal interpolation methods. Two land cover data sets were compiled at the national scale; one by the Multi-Resolution Land Characteristics Consortium (the National Land Cover Dataset or NLCD) and one by the Coastal Change Analysis Program. A third land cover data set was compiled at a regional scale for the state of Connecticut by the Center for Land Use Education and Research. Results show that for areal interpolation, greater detail in the classification of developed areas was important whether the data were developed for use at a national or a regional scale. Even more important is the further enhancement of remotely sensed land u...

The spatial correlation, or colocation, of two or more variables is a fundamental issue in geographical analysis but has received much less attention than the spatial correlation of values within a single variable, or autocorrelation. A recent paper by Leslie and Kronenfeld (2011) contributes to spatial correlation analysis in its development of a colocation statistic for categorical data that is interpreted in the same way as a location quotient, a frequently used measure in human geography and other branches of regional analysis. Geographically weighted colocation measures for categorical data are further developed in this article by generalizing Leslie and Kronenfeld's global measure as well as specifying a local counterpart for each global statistic using two different types of spatial filters: fixed and adaptive. These geographically weighted colocation quotients are applied to the spatial distribution of housing types to demonstrate their utility and interpretation.

Location quotients (LQs) are commonly used descriptive statistics in spatial analysis. They are not directly affected by neighbor relationships, and their calculation ignores spatial information. This article computes LQs as focal, rather than strictly local, functions by incorporating the spatial structure of the observations in the reference group in their computation. Focal LQs, tested for significance by event-based Monte Carlo simulations, are applied both to North Carolina sudden infant death syndrome data for comparison with the spatial statistic and to sectoral employment data in that state in a more typical context for LQ analysis. The significance tests show that the focal LQ is far more sensitive to the size of the denominator for spatially intensive data than is the statistic. Los autores usan momentos de una matriz de covarianza para datos panel espaciales para estimar los parametros del modelo de autoregresion espacial (spatial autoregressive model), incluyendo la matriz de conectividad (o de ponderacion) espacial W. En el modelo de autoregresion espacial sin restricciones, los parametros estan sub-identificados por un valor de uno en los casos que la matriz W es simetrica. Los autores demuestran que existe un caso especial en el cual la matriz W es asimetrica y sus parametros tienen calculo exacto. Si los datos panel son estacionarios y ergodicos, espacial y temporalmente, los estimados de W y el coeficiente de autoregresion espacial son consistentes. Para ilustrar la metodologia propuesta, los autores usan datos-panel espaciales de precios de vivienda en Israel. 区位熵（LQs）通常应用于空间分析的统计性描述。但分析时并没有考虑来自邻域相关性的直接影响，并且计算过程忽略空间信息。本文计算局部焦点的LQs，而并非严格意义上的局部空间，在计算过程的对照组中，对观察值的空间结构进行合并生成函数。对于局部焦点LQs的显著性检验采取基于事件的蒙特卡洛模拟方法，同时应用于北卡罗来纳州的婴儿猝死综合症（SIDS）数据与Gi*空间统计对照，和更典型状况下该州就业部门数据的LQ分析。显著性检验表明局部焦点LQ在数据密集的空间尺度上比Gi*统计量更为敏感。

Areal interpolation has been developed to provide attribute estimates whenever data compilation or an analysis requires a change in the measurement support. Over time numerous approaches have been proposed to solve the problem of areal interpolation. Quantile regression is used in this study as the basis of the areal interpolator because it provides estimates conditioned on local parameters rather than global ones. An empirical case study is provided using a data set in northern New England. Land cover data, provided by the National Oceanic Atmospheric Administration, derived from remotely sensed images for 2001 captured by the LANDSAT Thematic Mapper at a resolution of 30 × 30 meters, are used for the ancillary variables for the regression model. The utility of quantile regression as an areal interpolation method is evaluated against simple averages, areal weighting, dasymetric interpolation, and ordinary least squares and spatial regression methods. For the empirical data set used in the study, results ...

Contrails are important in understanding how aviation potentially affects climate change. The commonly used methods for detecting contrails usually only consider the spectral information of the image. This study focuses on introducing an object-based method for detecting contrails in AVHRR images. An object-based method utilizes not only the spectral characteristics, but also spatial information to aid in the detection. The results of the experimental study demonstrate that it is practical to use an object-based classification method for contrail detection. Compared with pixel-based classification methods, the accuracy of the results is improved using the object-based method.

Hanink D. M., Ebenstein A. Y. and Cromley R. G. Spatial analysis of selected manufacturing and service sectors in China's economy using county employment data for 1990 and 2000, Regional Studies. This paper provides a comparative analysis of the spatial distribution of employment in forty-one economic sectors in China in 1990 and in 2000. Sectors are approximately split between manufacturing and services. Spatial distributions of employment by sector are analysed at the county level, and relative sectoral specialization at the county level is also considered. Manufacturing and service clusters are identified in both years using factor analysis, and the resulting factor scores are used in mapping their spatial extent. In general, geographical concentration in Chinese manufacturing accelerated between 1990 and 2000, while services became more spatially uniform in their distribution. Hanink D. M., Ebenstein A. Y. et Cromley R. G. Une analyse geographique des secteurs de l'industrie et des services choisis de...

Wage-based evidence of returns to external scale in China’s manufacturing in 2004 is assessed using county-level units of observation. Two models are used: one with population and percent manufacturing employment as measures of urbanization and localization, respectively, and the other with population density and total manufacturing employment. Global results estimated from spatial error models and local results from geographically weighted regressions indicate that urbanization economies of scale, measured in terms of population but not population density, and localization economies of scale, measured in terms of percent of employment in manufacturing or total manufacturing employment, are apparent—but only when internal scale of production is not included as an explanatory factor in the models. Economies of scale are still outcomes of the models, but only when the urbanization, localization, and internal scale factors are considered in combination. The results not only illustrate particular characteristics with respect to recent returns in China’s manufacturing, they are also interesting with respect to schools of thought concerning increasing returns as externality effects.

This paper develops a location-production-allocation model for the multiplant producer selling under the conditions of nonlinear production costs and a uniform pricing system. The primary goals of this paper are to incorporate the following criteria into a model: (1) production factors and location factors should be synthesized into a location-production-allocation problem; (2) the production function is nonlinear, implying economies and diseconomies of scale; (3) a systems approach should be utilized to solve for a global optimum. Within the context of these criteria, equilibrium conditions are derived for a location-production-allocation model. Finally, a heuristic technique to solve this problem is derived from the equilibrium conditions.

Digital line simplification operators are an important preprocessing component of a spatial data handling system. Simplification operators are used to generalize digital line files because these files are encoded at one scale but the objects they represent can be displayed at many other scales. Most line simplification algorithms are heuristics that caricaturize lines by eliminating points representing redundant data or conversely retaining the characteristic points of a line that have a high informational content. This paper presents a noninferior simplification algorithm that produces an optimal caricature of an encoded line with respect to the bandwidth criterion that all deleted points must be within a prespecified distance of the simplified line. Results show that it is a practical alternative to existing bandwidth heuristics.

This paper provides an examination of China's residential real estate market at the county level using data from that country's 2000 census. The market is a new one, having only been fully established in 1998. The analysis in the paper is in the form of an aggregate (county-level) hedonic model specified in two versions. Global parameters results are estimated using spatial error model specifications while more local effects are estimated by geographically weighted regression. Global results are typical in that structural characteristics such as floor space and contextual characteristics such as level of in-migration are important in residential prices. Local results, however, indicate significant spatial variation in the effect of both structural amenities and locational context on housing prices. In a simpler specification, rents are shown to respond positively to both median house prices levels and the supply of apartments available at market prices, but also with significant spatial variation across China.

Spatial shifts in the internal distribution of population are an important aspect of the dynamics of economic and demographic conditions in any country. Migration information is more complex than other place-specific population information, having both a temporal as well as a spatial component with respect to both origin and destination regions; and the collection of these data is less complete than the reporting of population by place in a national census. In this study, GIS and tabulation methods have been developed to estimate age- and sex-specific internal net migration patterns. These methods are applied to a case study of net migration patterns in China between 1990 and 2000 at the county level. Although migration data are reported at a county level there is no breakdown by age or sex. Estimates from the methods developed in this paper show an intra-provincial migration pattern which is creating dependency ratio imbalances between rural and urban areas in Eastern China.

When calculating the components of population change over time, the spatial units of analysis must remain constant. However, the boundaries of these units often change from one census to the next Another limiting factor is the absence of data values for the time period. Net migration figures might be available for a five year interval in a census but not for a twenty year interval. GIS and areal interpolation are used here to rectify boundary changes that occur from one census to the next and shift‐share analysis is used to estimate the components of population change from the census data. These methods are applied to a county level study of population change in China between 1982 and 2000.

This study proposes a neural network approach to solving areal interpolation scenarios, specifically the “nested hierarchy” problem. The neural network method presented adopts the approach taken by intelligent interpolation methods where ancillary spatial information is presented to assist in achieving more accurate results. For this study, the data to be estimated are total populations for census tracts and block groups in Hartford County, Connecticut. A number of neural network models are generated containing various combinations of ancillary spatial information. The neural-network-derived predictions are compared with the predicted populations derived from three existing interpolation methods: areal weighting, a dasymetric areal weighting approach using remote sensing data, and ordinary least squares (OLS) regression. For each scenario presented, the proposed neural network approach outperforms each of the existing methods.

A simple Weberian agglomeration is developed and then extended as an innovative fixed-charged, colocation model over a large set of locational possibilities. The model is applied to cases in which external economies (EE) arise due to colocation alone and also cases in which EE arise due to city size. Solutions to the model are interpreted in the context of contemporary equilibrium analysis, which allows Weberian agglomeration to be interpreted in a more general way than in previous analyses. Within that context, the Nash points and Pareto efficient points in the location patterns derived in the model are shown to rarely coincide. The applications consider agglomeration from two perspectives: one is the colocation behavior of producers as the agents of agglomeration and the other is the interaction between government and those agents in the interest of agglomeration policy. Extending the analysis to games, potential Pareto efficiency and Hicks optimality are considered with respect to side payments between producers and with respect to appropriate government incentives toward agglomeration.

International non-profit organizations have started to implement eco-labeling for credence attributes programs aimed to inform consumers about environmentally sound or “sustainable” production standards for various products. Using coffee labeled as “shade grown” or “bird friendly” as an example, this paper describes the impact such labeling programs may have on local land use patterns in coffee producing regions. Shade grown coffee farms should provide a variety of external benefits, including the preservation of biodiversity, carbon sequestration, the prevention of soil erosion and aquifer recharge. Those externalities, however, are not expected to have observable land use impacts unless they are capitalized in the coffee market. The prospect of market capitalization of externalities suggests the extension of the conventional von Thunen model to the calculation of social location rent. Using the maximization of social location rent as a criterion allows the externality effect to play a direct role in market-based land use allocation of land between eco-labeled shade grown coffee production and other activities.

Exploratory data analysis and geovisualization have opened new paths for displaying and analyzing data. Alternate views of the same data set provide more insight into the nature of the data. The choropleth map has been the primary display device for portraying the spatial distribution of statistical data aggregated over areal units. However, the standard choropleth map is not as informative with respect to the statistical distribution of the data. Data classification for classed choropleth maps, as well as the transformation of numeric values into lightness values, filters out much of the information regarding the underlying statistical distribution of the data. Traditionally, the map legend has the role of connecting lightness values to their numeric counterparts and providing some information regarding the statistical properties of the data. It is proposed here that a more versatile legend can be constructed based on the ogive cumulative frequency diagram. Ogive legends can present the overall statistic...

This article describes a multimarket von Thunen model that concerns change in the internal economic geography of two countries once trade develops between them. Using the principle of comparative advantage, an initial allocation of four industries is established across each of the countries based on local variation in factor endowments, regional demand at several centers, and internal transport costs. Once trade begins, reallocation of production in the four industries in each country is modeled as the response to comparative factor endowments across the countries, trade costs, transport costs, and differences in demand. The model's results indicate that geography matters in trade both within and across countries. Proximity is especially important. Regions of the trading countries that are effectively neighbors are affected more by the introduction of trade than are regions that are peripheral in the true geographical sense. At the national scale, results suggest that a high tariff in one country...

Traditionally, choropleth maps are used to examine spatial patterns present in a data distribution rather than to examine specific data values within that distribution. The transformation of initial numeric data values into graphic values for visual display differs for classed and unclassed maps. Classed maps first group data into classification intervals and each interval is then assigned a graphic value usually using the principle of maximum contrast. Unclassed maps skip the classification step and directly assign graphic value based on the original numeric data value. The paper examines the differences in defining algebraic-to-graphic transformation lines for both classed and unclassed classed maps and especially the role of the maximum contrast principle in constructing classed choropleth maps. Results show that the maximum contrast principle increases the visual complexity of spatial patterns in a classed map over the level present in a corresponding unclassed map.

Two American geographers analyze German interstate migration patterns during the period following unification of former West and East Germany (1990-2003). A specific focus of the analysis is the extent to which migration patterns established immediately following unification have changed over the ensuing years. The paper presents a variety of complementary analyses to establish the temporal and geographic patterns of German domestic migration and discusses possible reasons for the seemingly surprising stability of migration patterns throughout the period. Journal of Economic Literature, Classification Numbers: J61, O15, R23. 1 figure, 9 tables, 35 references, 3 appendices.

The Ricardian principle of comparative advantage is used frequently as a foundation for illustrating potential improvements in the allocation of production and trade flows among countries. Such improvements typically consist of increased economic efficiency in the form of lower aggregate production costs and greater welfare in the form of higher levels of aggregate output. The improvements are made possible by the effective integration of otherwise individual economies. This paper employs the principle of comparative advantage as the foundation for illustrating potential improvements in the allocation of land uses under a regional approach to land-use planning. It uses a generalized assignment problem within a geographical information system (GIS) to describe the efficiency benefits of integrating land-use plans among several districts.